Multienzyme preparation containing a blend of mannanase, xylanase, cellulase, pectinase, β-glucanase, amylase, protease, and phytase on the performance of laying hens fed corn–soybean meal diet
Bibliographic record
Abstract
This study evaluates the dose–response effects of a multienzyme blend on performance, egg and eggshell quality, caecal microflora, nutrient digestibility, and digesta viscosity in hens fed corn–soybean meal (SBM) diet. Three hundred, 23-week-old Hy-Line Brown hens (1.92 kg) from GANONGBIO breeding farms, South Korea, were randomly assigned to 5 dietary treatments (15 replicates, 4 hens/replicate). After a 14-day adaptation, hens received a corn–SBM basal diet (CON) or CON supplemented with 0.015% (ME1), 0.025% (ME2), 0.0375% (ME3), or 0.05% (ME4) multienzyme for 12 weeks. Multienzyme supplementation linearly improved ( p < 0.05) hen-day egg production, egg mass, and feed conversion at 12 weeks. Egg loss showed a quadratic effect ( p < 0.05) during 8 weeks, and a linear reduction ( p < 0.05) at 12 weeks. Haugh unit, yolk weight, and percentage showed a quadratic effect ( p < 0.05) at 12 weeks, while yolk cholesterol decreased linearly ( p < 0.05) at 8 weeks. Digestibility of dry matter, crude protein, gross energy, nonstarch polysaccharides, xylan, threonine, valine, and serine increased linearly ( p < 0.05). Lactobacillus spp. and Bifidobacterium spp. increased ( p < 0.05), while Escherichia coli and coliforms decreased ( p < 0.05). Multienzyme inclusion from ME1 to ME4 amplified prebiotic and bioactive effects within the gastrointestinal tract, resulting in enhanced nutrient metabolism, improved gut health, and performance in laying hens.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".